Missing Data imputation. Another application of stochastic representation learning is to provide distributions over unobserved values [1]}. In many application domains, sensor readings go missing, which we may mimic by letting parts of an image be unobserved. [1]} show that we can then draw samples from the distribution of the entire image conditioned on the observed part, by imputing the missing pixels with noise and repeatedly encode and decode while keeping observed pixels fixed. fig:dataimputation show samples using this procedure from a vae, a post-hoc lae and our online lae, where we only observe the lower half of an mnist image. This implies ambiguity about the original digit, e.g. the lower half of a “5“ could be a “3“ and similarly a “7“ could be a “9“. Our lae captures this ambiguity, which is exemplified by the multi-modal reconstructions from the sampled networks in fig:dataimputation. The baselines only capture unimodal reconstructions.
